Abstract. ML-kNN is a well-known algorithm for multi-label classifica-tion. Although effective in some cases, ML-kNN has some defect due to the fact that it is a binary relevance classifier which only considers one label every time. In this paper, we present a new method for multi-label classification, which is based on lazy learning approaches to classify an unseen instance on the basis of its k nearest neighbors. By introducing the coupled similarity between class labels, the proposed method exploits the correlations between class labels, which overcomes the shortcoming of ML-kNN. Experiments on benchmark data sets show that our pro-posed Coupled Multi-Label k Nearest Neighbor algorithm (CML-kNN) achieves superior performance than some existing multi-label classifica-tion algorithms.

Abstract. Recommender systems research has experienced different stages such as from user preference understanding to content analysis. Typical recommenda-tion algorithms were built on the following bases: (1) assuming users and item-s are IID, namely independent and identically distributed, and (2) focusing on specific aspects such as user preferences or contents. In reality, complex recom-mendation tasks involve and request (1) personalized outcomes to tailor hetero-geneous subjective preferences; and (2) explicit and implicit objective coupling relationships between users, items, and ratings to be considered as intrinsic forces driving preferences. This inevitably involves the non-IID complexity and the need of combining subjective preference with objective couplings hidden in recom-mendation applications. In this paper, we propose a novel generic coupled matrix factorization (CMF) model by incorporating non-IID coupling relations between users and items. Such couplings integrate the intra-coupled interactions within an attribute and inter-coupled interactions among different attributes. Experimental results on two open data sets demonstrate that the user/item couplings can be effectively applied in RS and CMF outperforms the benchmark methods. 1